Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry
This addresses the challenge of accurate 6-DoF visual localization for robots in long-term navigation, though it appears incremental as it builds on existing deep learning approaches for VO.
The paper tackles the drift problem in long-term visual odometry by proposing deep end-to-end networks that fuse relative and global sub-networks based on RCNNs, improving monocular localization accuracy and outperforming state-of-the-art learning-based methods on indoor and outdoor datasets.
Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global networks based on Recurrent Convolutional Neural Networks (RCNNs) to improve the monocular localization accuracy. Indeed, the relative sub-networks are implemented to smooth the VO trajectory, while global subnetworks are designed to avoid drift problem. All the parameters are jointly optimized using Cross Transformation Constraints (CTC), which represents temporal geometric consistency of the consecutive frames, and Mean Square Error (MSE) between the predicted pose and ground truth. The experimental results on both indoor and outdoor datasets show that our method outperforms other state-of-the-art learning-based VO methods in terms of pose accuracy.